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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

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The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
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Instrument Transformers01:23

Instrument Transformers

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Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
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Transformers in Distribution System01:27

Transformers in Distribution System

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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Energy Losses in Transformers01:21

Energy Losses in Transformers

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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
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The Ideal Transformer01:26

The Ideal Transformer

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In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
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Area of Science:

  • Acoustics
  • Signal Processing
  • Machine Learning

Background:

  • Traditional microphone array methods for sound source characterization often rely on focus-grids, leading to high computational costs or limited accuracy.
  • Existing grid-free methods may require multiple models for varying numbers of sound sources.

Purpose of the Study:

  • To develop a deep learning-based, grid-free method for accurate sound source characterization.
  • To enable a single model to identify an unknown number of sound sources simultaneously.
  • To overcome the computational demands and accuracy limitations of conventional grid-based approaches.

Main Methods:

  • A Transformer deep learning architecture was developed and trained exclusively on simulated acoustic data.
  • The model predicts spatially clustered source components, enabling individual source strength determination through integration.
  • Strategies for reducing training effort across different frequencies were investigated.

Main Results:

  • The method demonstrated fast and accurate source mapping for up to ten sound sources across various frequencies.
  • Performance was validated against established methods like CLEAN-SC and sparse Bayesian learning using experimental data.
  • The approach successfully characterized individual source strengths by integrating predicted cluster components.

Conclusions:

  • The proposed deep learning method offers a computationally efficient and accurate alternative for grid-free sound source characterization.
  • The single-model architecture effectively handles an unknown number of sources, outperforming traditional and some existing grid-free techniques.
  • This approach holds significant potential for applications requiring precise acoustic source identification without grid-based constraints.